Companies increasingly automate their business processes, and more importantly, they automate more of the lower level tasks involved. Currently, companies are required by internal and external regulations such as Sarbanes-Oxley, HIPPA and the Patriotic Act to maintain accurate bookkeeping that documents processes, activities, procedures and business reporting. In response, organizations turn to solutions like Business Activity Monitoring (“BAM”) to automate and control their business processes.
In addition to the necessity for compliance with new federal regulations, automated monitoring of business processes also leads to an increase in productivity. Since events flow between several enterprise layers, the events can be used to provide an integrated view of various components of the layers. Events resemble the blood cells flowing through the entire system, carrying information and sustaining the entire process
The following discussion illustrates some of the challenges introduced by a complex monitoring system that fully exploits events. The first challenge is scalability with respect to event sources and monitors. For example, consider the effects on event management that arise due to requirements of complex monitoring applications. Events flow between various architectural layers, and they are subsequently stored and retrieved for monitoring-related tasks of analytical processing. As a result of the automation of business process tasks, there is an increase in the number of events that are produced and are necessary for analysis. At the same time, the requirements for increasingly complex queries over these events also escalate. These processes compete for the same event-management resources. Another effect of the growing number of events that flow through the system is the congestion of the network and computational resources.
Note that reducing the load on the event-management storage by allowing only simple queries is not an option, since it leads to a decrease in features and potential of the monitoring system. Another challenge is with event storage and query contention. The result of increasing the number and detail of automated business tasks is a greater number of events. At a minimum, the events that contribute to the calculation of essential key performance indicators (“KPIs”) should be stored for further analysis. This information is essential in understanding the provenience of the problems the metrics indicate. While the quantity of events to be stored increases, the number and complexity of queries over events also increases. Since event-management databases have to support both updates and queries, they become the bottleneck of the entire system
Yet another challenge is with network and computational resources. For example, many current complex monitoring systems experience network and middleware congestion from the growth in the number and rate of events generated by business processes. These complex monitoring systems also perform useless computations at the application level. Events that are not necessary to the computation of metrics still need to be processed and filtered, which may lead to another potential bottleneck. Redundant computations are also performed by many of the current monitoring systems. Filtering steps can include computation that is redundant between different monitoring contexts and even between different monitors.
Therefore a need exists to overcome the problems with the prior art as discussed above.